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WGS for the 100,000

Genomes

Mark T. Ross

Population and Medical Genomics Group

Applying Genomics to Cancer, 21 Sept 2015

2

100,000 genomes

Cancer and rare genetic disease

ISO-accredited workflow (2016)

Data delivered electronically,

stored securely and analysed in

England Data Centre

Combine with extracted clinical

information for analysis,

interpretation, and aggregation

Genomics England Partnership

3

A Clinical Ecosystem for Precision Medicine

Patient information Treatment choice

Knowledge base Researcher Clinician

Timeline

Birth Biopsy

Surgery

Relapse

Event Metastasis

Chemo Relapse

Sequencing

Death

4

Fast WGS for genetic diagnosis of intensive care patient

Undiagnosed condition: – Male child presents at 5 months with developmental regression,

hypotonia, and seizures

One WGS test: DNA sample to answer in 4 days

Filter annotations rapidly using generic queries: Number of transcripts with functional variants: 13,367

‘With 2 variants’** and <5% allele frequency: 1,458

And predicted to be functional: 287

And in a gene linked to disease: 35

And predicted to be deleterious: 21

Evolutionarily conserved: 6

Apply control genomes filter: 1

Confirm Menkes diagnosis – A novel hemizygous variant in ATP7A, a gene with mutations

known to disrupt copper metabolism

Kingsmore et al. (Children’s Mercy Hospital)

** shorthand for homozygous., compound heterozygous, hemizygous positions

5

miR-26a-2 PTEN

PDGFRA

c-kit

PI3K AKT Growth

44-year old with glioblastoma recurrence

Not responding to treatments

Sequence genomes from three biopsies and normal genome

WGS of brain tumour defines new treatment options

Swanton et al. (CRUK London Research Institute)

DNA rearrangements amplify

growth regulator genes

PDGFRA gene

normal

amplified

New treatment indicated

(10 days from start)

PDGFRA

c-kit

Amplify

+ + +

+

Growth

C-kit

miR-26a-2

Cancer

+

Cancer

Imatinib

Sunitinib

Pazopanib

6

Trinucleotide contexts around somatic mutations reveal signatures of exposure

Mutation context spectra reveal catastrophic events - kataegis

Genome-wide mutation signatures in cancer

Alexandrov et al. 2013 Sanger

Smoking: Most are NCN>NAN

UV: Most are TCN>TTN

Smoking

UV light

Alexandrov et al. 2013

Sequence context

Nik-Zainal et al. 2012

Context and density

C>T, 14Mb on chr 6 of a BRCA1 mutated tumour

7

Why genomes?

Working with clinical samples

Maximise throughput, coverage and accuracy

Shrink the data footprint

Annotation, interpretation, reporting

Scale up: a fast, convenient, high throughput ISO workflow

Aggregate the information for maximum value

Evolving the technology for medicine

Sequence Analyse Annotate Interpret Answer Sample

8

Simple library prep: reproducible, automated, low cost, low hands-on, fast (4 hrs)

Minimum bias: PCR-free, recovers all genome (reference and non-reference)

Low input DNA: maximises access to clinical samples

Maximum coverage: (depth dependent)

– 30x genome (110 Gb): >96% of hg19 genome ~98% of e! exons

All variant types: all variant types can be detected; both novel and known

Questions/trade-offs:

– Cost, data volume/management, study size (n)

– Sensitivity, diagnostic yield, long term value

Why focus on whole genomes?

Large

SVs

Balanced

translocation

Distant

consanguinity

Uniparental

disomy

Coding

variants

Non-coding

variants

Panel Knowns Knowns No No Yes Knowns

Exome Partial No Partial Partial Yes No

Genome Yes Yes Yes Yes Yes Yes

The complete, accurate genetic make-up of an individual

9

Improving coverage and reducing bias

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

1ug PCRv2

1ug PCR

500ng PCR Free

100ng PCR free Sample 1

100ng PCR free Sample 2

HiSeqX PCRFree (V2)

No

rmalised

Gen

om

e C

ov

era

ge

1 ug PCR v2 clusters

1 ug PCR

500 ng PCR-free (gel)

PCR-free (beads)

PCR-free (beads)

PCR-free on X (beads)

ARX exon 2, 77% GC / HiSeq X

Nano v1

Nano v2

PCR-free

10

Improving technology

Patterned flowcells for higher density (2x)

Faster imaging for greater speed (6x)

Improved SBS chemistry for accuracy &

readlength (99.8-99.9% cycle efficiency)

Increasing data rate & reducing cost

0

2

4

6

8

10

2010 2011 2012 2013 2014

WGSPrice($k/30x)

0

100

200

300

400

500

600

2010 2011 2012 2013 2014

DataRate(Gb/day)

2 um features 0.4 um features

(images to same scale)

11

Assessing accuracy

Variant calling accuracy based on Pt truth sets

– enable objective algorithm performance assessment & improvement

Sensitive to depth and quality of alignment

Very low false positive SNP or indel call rate

Pipeline SNV (0 bp) Indel (1-50 bp) Time

(hr)**

Sn (%) Sp (%) Sn (%) Sp (%)

HAS (Isaac) (H2’15) 96.9 99.8 92.3 98.3 5

GATK 3.2 98.1 99.9 88.6 98.9 38

**Using 40 CPU, Intel Xeon @ 2.80 GHz, 132 GB RAM**

12

Shrinking the data footprint

Archive &

Aggregate

1 Reduced Resolution Quality Scores 2 Genome VCF – encodes all positions and low quality calls

Archive &

Look-up

Temporary

Or R&D

iPad

Plug

And

Play

Cloud

gVCF2

2G

Annotated

gVCF

2G

CRAM

30G

RRQS1

BAM

80G 120G

BAM

13

Adding comprehensive annotation

Capture publicly available/licensed information (Ve!P-based)

– Genes: Nomenclature, coordinates, transcripts (ensembl, NCBI)

– Functional effects: VEP consequence, regulatory regions (ensembl, UCSC, Encode)

– Population information: 1000-genomes, EVS

– Disease association: Clinvar, COSMIC, PharmGKB (+ manual review)

Accelerate and improve efficiency of annotation process

– Streamline reporting algorithms and data storage structures

– Speed: From 11 hr 48 min to 3 min per genome

– Footprint: From 2+8 Gb Cache+RAM to 0.23+0.3 Gb per genome

How do we create medically useful genomes?

14

Tumour and normal genomes

Extra depth of the tumour genome sequence

Sample purity, quality and quantity

Variability in fixation and extraction processes

Evolution of disease and heterogeneity

Somatic variant calling, annotation, interpretation, reporting

Evolving the technology for cancer medicine

Sequence Analyse Annotate Interpret Answer Sample

15 Schuh et al., Oxford

CLL WGS timeseries

Heterogeneity, treatment response

Remission sample has “disease”

Disease evolution and heterogeneity

So

ma

tic

mu

tati

on

fre

qu

en

cy (

%)

0

50

100

150

200

250

a b c d e

NORMAL

CLASS4

CLASS3

CLASS2

CLASS1

Time points

Abundance

0

1

2

3

4

5

c

NORMAL

CLASS4

CLASS3

CLASS2

CLASS1

R

16

Secondary Analysis Workflows

Isaac aligner

BAM

file

Sequencing data

Isaac Diploid

Variant Calling

(Starling)

VCF

germline

SNVs and

indels

Canvas

VCF

germline

CNVs

Manta

germline

VCF

germline

SVs

Firebrand

Metrics

Normal BAM

+

Tumour BAM

Seneca

VCF

somatic

CNVs

Manta

somatic

VCF

somatic

SVs

Strelka

VCF

somatic

indels

VCF

somatic

SNVs

Germline variants Somatic variants

17

Excerpts from colorectal cancer Encore report

1

2

18

Workflow: DNA to annotated Genome

Sample Accession

Sample Quant Quality Control, FFPE

check Library Prep

Library Quality Control qPCR

X10 Sequence Run + QC

Genome build, tumour / normal subtractiongVCF

annotation HiSeq Analysis Software

Analysis Quality Control, identity

check, contamination screen

Network Delivery

Automation + 96 well format

Flowcell prep Genotype

pre

LIMs

Project configured

Track lab and analysis processes

Project management, Pipeline

automation

Library Amp

(if needed)/

Genotype post

Pre-PCR lab

19

Rare genetic disease samples (18th Sept 2015)

4 samples failed on DNA quantity

1 sample contaminated

30 samples on-hold awaiting resolution of inconsistent gender-manifest information

Status of initial pipeline

Ge

no

me

s (

n)

384 4 193 400

6955

31 0

1000

2000

3000

4000

5000

6000

7000

8000

DNA in QC Fail QC Sequencing Analysis Delivered Fail/on hold

Genomes n=7,967

20

Evaluate utility of genome sequencing in healthcare (Genomics England)

A powerful lens for the patient and personalised medicine

Early applications in rare genetic disease and cancer (40% of population)

Population-level screening for systematic studies

Aggregate G, and G+P data to add value (standard, secure, accessible)

Integrate with targeted testing, e.g. pre-natal, MRD and other tests

Future Prospects

Researcher

Treatment choice

Clinician

Patient

Knowledge

Information

21

Acknowledgements

David Bentley

Sean Humphray

Elliott Margulies

Mike Eberle

Ryan Taft

Lisa Murray

Klaus Maisinger

Come Raczy

Semyon Kruglyak

Stewart MacArthur

Philip Tedder

John Peden

Roman Petrovski

Kevin Hall

Keira Cheetham

Jennifer Becq

Miao He

Russell Grocock

Peter Saffrey

Josh Bernd

Richard Shaw

Chris Saunders

Shankar Ajay

Pedro Cruz

Jason Betley

Jacqueline Weir

Zoya Kingsbury

Core Sequencing Group

David Quackenbush

Eddy Kim

Van Lee-Pham

Anna Powell

Francisco Garcia

Kirby Bloom

Tina Hambuch

Erica Ramos

& many others

Illumina The team at Genomics England

Stephen Kingsmore and the Children’s Mercy

Hospital, Kansas City team

Charles Swanton and team, CRUK London

Research Institute

Anna Schuh, Jenny Taylor and the WGS500

consortia, Oxford

Lisa Russell, Christine Harrison and team,

LRCG Newcastle

Mike Stratton and the Cancer Genome Project

team, Wellcome Trust Sanger Institute, Hinxton

Gil McVean and Zamin Iqbal, WTCHG, Oxford

Jan Veldink and team, UMC Utrecht

Willem Ouwehand, Lucy Raymond and the

UCAM Project team, Haematology,

Addenbrookes, Cambridge

Andrew Beggs and team, Cancer Sciences,

Birmingham

Collaborators